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[Preprint]. 2024 Jul 27:2024.07.26.605378.
doi: 10.1101/2024.07.26.605378.

Limiting the impact of protein leakage in single-cell proteomics

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Limiting the impact of protein leakage in single-cell proteomics

Andrew Leduc et al. bioRxiv. .

Update in

Abstract

Limiting artifacts during sample preparation can significantly increase data quality in single-cell proteomics experiments. Towards this goal, we characterize the impact of protein leakage by analyzing thousands of primary single cells that were prepared either fresh immediately after dissociation or cryopreserved and prepared at a later date. We directly identify permeabilized cells and use the data to define a signature for protein leakage. We use this signature to build a classifier for identifying damaged cells that performs accurately across cell types and species.

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Conflict of interest statement

Competing interests: N.S. is a founding director and CEO of Parallel Squared Technology Institute, which is a nonprofit research institute.

Figures

Figure 1 |
Figure 1 |. Quantifying protein leakage artifacts.
a, Images of cells in the CellenONE nozzle taken with brightfield and with the green fluorescent channel. The two cells shown are not clearly distinguishable from the brightfield but one cell is positive for the sytox green fluorescent dye. b, The UMAP dimensionality reduction shows co-clustering of cells from fresh and frozen batches that were recorded as permeable via Sytox green. c, Percentages of permeable cells for the two sample handling conditions are shown in the dot plot. d, Differences in protein fold changes between permeable and intact cells for all proteins. e, Differences in protein fold changes between permeable and intact are categorized by subcellular compartment.
Figure 2 |
Figure 2 |. Examining human cell line data.
a, Log2 average protein fold changes between permeable and intact Club cells and Fibroblasts are plotted against each other and show pearson correlation of 0.66. A heatmap summarizing correlations between fold changes for all cell types is shown. b. ROC curve for a classifier trained on permeability status of single cells using protein abundance profiles of the top 75 most significantly leaking proteins. Results for the model trained and tested on the same cell type are in red and trained and tested on different cell types are in black. c. PCA projection of single cells from Leduc et al. 2024. Cells are colored by cell type or their permeability score from the classifier. The cells towards the center of the two dimensional space of the first two principal components were enriched for high permeability score. d, When comparing the fold changes of cells with probability over 0.2 and under 0.2 to permeable versus intact fold changes from the primary mouse tracheal cells, the fold changes strongly agree, pearson correlation 0.50.

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References

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